Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/45520
Title: Multiple Model Ballistic Missile Tracking With State-Dependent Transitions and Gaussian Particle Filtering
Authors: Yu, Miao
Gong, Liyun
Oh, Hyondong
Chen, Wen-Hua
Chambers, Jonathon
First Published: 13-Nov-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE) for Aerospace and Electronic Systems Society
Citation: IEEE Transactions on Aerospace and Electronic Systems, 2018, 54 (3), pp. 1066-1081
Abstract: This paper proposes a new method for tracking the entire trajectory of a ballistic missile from launch to impact on the ground. Multiple state models are used to represent the different ballistic missile dynamics in three flight phases: boost, coast, and re-entry. In particular, the transition probabilities between state models are represented in a state-dependent way by utilizing domain knowledge. Based on this modeling system and radar measurements, a state-dependent interacting multiple model approach based on Gaussian particle filtering is developed to accurately estimate information describing the ballistic missile such as the phase of flight, position, velocity, and relevant missile parameters. Comprehensive numerical simulation studies show that the proposed method outperforms the traditional multiple model approaches for ballistic missile tracking.
DOI Link: 10.1109/TAES.2017.2773258
ISSN: 0018-9251
Links: https://ieeexplore.ieee.org/document/8106676
http://hdl.handle.net/2381/45520
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2017. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Engineering

Files in This Item:
File Description SizeFormat 
08106676.pdfPublished (publisher PDF)1.36 MBAdobe PDFView/Open


Items in LRA are protected by copyright, with all rights reserved, unless otherwise indicated.